arXiv：cs.AI· Mary Tonwe·· 19 小时前
OPTIC-ER：面向非洲欠发达社区实时应急响应的强化学习框架
OPTIC-ER: A Reinforcement Learning Framework for Real-Time Emergency Response and Equitable Resource Allocation in Underserved African Communities
AI 导读
OPTIC-ER 是一个基于注意力引导 actor-critic 架构的强化学习框架，用于实时、自适应且公平的应急调度。在尼日利亚河流州真实数据的高保真仿真中，该系统在 500 起未见事件上将应急响应时间缩短超过 35%，并超过最近设施启发式 62.94% 的最优选择率。它还能生成基础设施缺陷地图与公平性监测仪表盘，并基于 TALS 框架适配低资源部署。
正文
Abstract:Public service systems in many African regions suffer from delayed emergency response and spatial inequity, causing avoidable suffering. This paper introduces OPTIC-ER, a reinforcement learning (RL) framework for real-time, adaptive, and equitable emergency response. OPTIC-ER uses an attention-guided actor-critic architecture to manage the complexity of dispatch environments. Its key innovations are a Context-Rich State Vector, encoding action sub-optimality, and a Precision Reward Function, which penalizes inefficiency. Training occurs in a high-fidelity simulation using real data from Rivers State, Nigeria, accelerated by a precomputed Travel Time Atlas. The system is built on the TALS framework (Thin computing, Adaptability, Low-cost, Scalability) for deployment in low-resource settings. In evaluations on 500 unseen incidents, OPTIC-ER achieved a greater than 35% reduction in emergency response times and outperformed the 62.94% optimal-selection rate of the nearest-facility heuristic, confirming its robustness and generalisation. Beyond dispatch, the system generates Infrastructure Deficiency Maps and Equity Monitoring Dashboards to guide proactive governance and data-informed development. This work presents a validated blueprint for AI-augmented public services, showing how context-aware RL can bridge the gap between algorithmic decision-making and measurable human impact.
| Comments: | Source code and data available at: this https URL |
| Subjects: | Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG) |
| Cite as: | arXiv:2508.12943 [cs.AI] |
| (or arXiv:2508.12943v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2508.12943 arXiv-issued DOI via DataCite |
Submission history
From: Mary Tonwe [view email]
[v1]
Mon, 18 Aug 2025 14:19:57 UTC (2,375 KB)
[v2]
Thu, 4 Dec 2025 15:30:54 UTC (2,392 KB)
[v3]
Sun, 11 Oct 2026 02:59:16 UTC (2,392 KB)
来源：arXiv：cs.AI · arxiv.org